# this cell is tagged as parameters for `papermill` parameterization
# input configs
altair_config = None
nipah_config = None

# input files
binding_E2_file = None
binding_E3_file = None

# output images
entry_binding_combined_corr_plot = None
E2_E3_correlation = None
E2_E3_correlation_site = None
binding_by_site_plot = None
entry_binding_corr_heatmap = None
binding_region_bubble_plot = None
combined_contact_ranked_bar_output = None

# Parameters
nipah_config = "nipah_config.yaml"
altair_config = "data/custom_analyses_data/theme.py"
binding_E2_file = "results/filtered_data/binding/e2_binding_filtered.csv"
binding_E3_file = "results/filtered_data/binding/e3_binding_filtered.csv"
entry_binding_combined_corr_plot = (
    "results/images/entry_binding_combined_corr_plot.html"
)
E2_E3_correlation = "results/images/E2_E3_correlation.html"
E2_E3_correlation_site = "results/images/E2_E3_correlation_site.html"
binding_by_site_plot = "results/images/binding_by_site_plot.html"
entry_binding_corr_heatmap = "results/images/entry_binding_corr_heatmap.html"
binding_region_bubble_plot = "results/images/binding_region_bubble_plot.html"
combined_contact_ranked_bar_output = (
    "results/images/combined_contact_ranked_bar_output.html"
)

import math
import os
import re
import altair as alt
import numpy as np
import pandas as pd
import scipy.stats
import yaml

# allow more rows for Altair
_ = alt.data_transformers.disable_max_rows()

if (
    os.getcwd()
    == "/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/"
):
    pass
    print("Already in correct directory")
else:
    os.chdir(
        "/fh/fast/bloom_j/computational_notebooks/blarsen/2023/Nipah_Malaysia_RBP_DMS/"
    )
    print("Setup in correct directory")

Setup in correct directory

if nipah_config is None:
    ##hard paths in case don't want to run with snakemake
    print("loading hard paths")
    altair_config = "data/custom_analyses_data/theme.py"
    nipah_config = "nipah_config.yaml"

    # input files
    binding_E2_file = "results/filtered_data/binding/e2_binding_filtered.csv"
    binding_E3_file = "results/filtered_data/binding/e3_binding_filtered.csv"

if altair_config:
    with open(altair_config, "r") as file:
        exec(file.read())

with open(nipah_config) as f:
    config = yaml.safe_load(f)

# import binding data
df_E2_filter = pd.read_csv(binding_E2_file)
display(df_E2_filter.head(3))
df_E3_filter = pd.read_csv(binding_E3_file)
display(df_E3_filter.head(3))

## Merge the filtered EFNB2 and EFNB3 DataFrames for combined analysis.
df_binding_effect_merge = pd.merge(
    df_E2_filter,
    df_E3_filter,
    on=["site", "wildtype", "mutant"],
    suffixes=["_E2", "_E3"],
    how="outer",
)
display(df_binding_effect_merge.head(3))
## Add a 'selection' column to distinguish between EFNB2 and EFNB3 data.
df_E2_filter["selection"] = "bEFNB2"
df_E3_filter["selection"] = "bEFNB3"

## Concatenate the DataFrames for plotting or further analysis.
df_binding_effect_concat = pd.concat([df_E2_filter, df_E3_filter])
display(df_binding_effect_concat.head(3))

# What are the top 5 highest and lowest binding mutants for EFNB2 and EFNB3?
def find_highest_lowest(df, name):
    print(f"We are analyzing {name}\n")
    print(f"The total number of mutants was: {df.shape[0]}\n")
    tmp_df = df.sort_values(by="binding_mean")
    print("These are the lowest binding mutants detected:")
    display(tmp_df.head(5))

    tmp_df_high = df.sort_values(by="binding_mean", ascending=False)
    print("\nThese are the highest binding mutants detected:\n")
    display(tmp_df_high.head(5))

    # What about mutants with positive entry scores?
    tmp_df = df[df["effect"] > 0].sort_values(by="binding_mean")
    print("These are the lowest binding mutants detected with positive entry scores:")
    display(tmp_df.head(5))

    tmp_df_high = df[df["effect"] > 0].sort_values(by="binding_mean", ascending=False)
    print(
        "\nThese are the highest binding mutants detected with positive entry scores:\n"
    )
    display(tmp_df_high.head(5))

    mean_df = df.groupby('site')['binding_mean'].sum().reset_index()
    print('These are the sites with the highest summed binding score:\n')
    display(mean_df.sort_values(by='binding_mean',ascending=False).head(10))


find_highest_lowest(df_E2_filter, "bEFNB2")
find_highest_lowest(df_E3_filter, "bEFNB3")

We are analyzing bEFNB2

The total number of mutants was: 6632

These are the lowest binding mutants detected:

These are the highest binding mutants detected:

These are the lowest binding mutants detected with positive entry scores:

These are the highest binding mutants detected with positive entry scores:

These are the sites with the highest summed binding score:

We are analyzing bEFNB3

The total number of mutants was: 6503

These are the lowest binding mutants detected:

These are the highest binding mutants detected:

These are the lowest binding mutants detected with positive entry scores:

These are the highest binding mutants detected with positive entry scores:

These are the sites with the highest summed binding score:

def overall_stats(df, effect, name):
    # Now group sites and find sites where all mutants are deleterious
    filtered_df = df.groupby("site").filter(lambda group: (group[effect] < -0.25).all())
    # Which sites are these?
    unique = filtered_df["site"].unique()
    # Convert unique to a Pandas Series
    unique_series = pd.Series(unique)

    # Find the common elements that are also contact sites
    unique_contact_bool = unique_series.isin(config["contact_sites"])
    # Filter and get the common elements
    common_elements = unique_series[unique_contact_bool]

    print(f"The dataset we are analyzing is: {name}\n")

    # Print the common elements
    print(
        f"Here are the contact sites that only have negative binding scores: {list(common_elements)}\n"
    )

    print(f"There are {len(unique)} sites with all negative binding score mutants\n")
    print(
        f"These are the sites with all negative binding score mutants: {list(unique)}\n"
    )

    # Now find sites with low and high binding (median)
    median_df = (
        df.groupby("site")["binding_mean"]
        .max()
        .reset_index()
        .sort_values(by="binding_mean", ascending=False)
    )
    print("These are the sites with the highest binding mutants:\n")
    display(median_df.head(5))

    # Now calculate mutant number
    total_mutants = df.shape[0]

    mutants_above_cutoff_tolerated = df[df["effect"] > 0]
    mutants_above_cutoff_tolerated = mutants_above_cutoff_tolerated[
        ["site", "effect", "binding_mean", "wildtype", "mutant"]
    ]

    total_sites = df["site"].unique().shape[0]

    print(f"The total number of sites are: {total_sites}")


overall_stats(df_E2_filter, "binding_mean", "EFNB2")
overall_stats(df_E3_filter, "binding_mean", "EFNB3")

The dataset we are analyzing is: EFNB2

Here are the contact sites that only have negative binding scores: [239, 242, 389, 488, 490, 491, 501, 504, 505, 531, 532, 533, 557, 579, 581, 588]

There are 43 sites with all negative binding score mutants

These are the sites with all negative binding score mutants: [100, 116, 220, 236, 238, 239, 242, 243, 248, 346, 351, 352, 389, 390, 399, 400, 435, 438, 441, 460, 467, 486, 487, 488, 490, 491, 494, 495, 497, 501, 504, 505, 526, 531, 532, 533, 557, 579, 581, 584, 585, 588, 590]

These are the sites with the highest binding mutants:

The total number of sites are: 507
The dataset we are analyzing is: EFNB3

Here are the contact sites that only have negative binding scores: [389, 488, 501, 505, 531, 532]

There are 16 sites with all negative binding score mutants

These are the sites with all negative binding score mutants: [108, 140, 352, 389, 467, 486, 488, 494, 495, 497, 501, 505, 510, 531, 532, 584]

These are the sites with the highest binding mutants:

The total number of sites are: 503

# find sites that are different
def find_biggest_differences(df):
    efnb2_good_efnb3_bad = df[
        (df["binding_mean_E2"] > 0.5) & (df["binding_mean_E3"] < -0.5)
    ].sort_values(by="binding_mean_E2", ascending=False)
    print('mutantions good for efnb2 binding, bad for efnb3 binding:\n')
    display(efnb2_good_efnb3_bad)

    efnb2_bad_efnb3_good = df[
        (df["binding_mean_E2"] < -0.5) & (df["binding_mean_E3"] > 0.5)
    ].sort_values(by="binding_mean_E3", ascending=False)
    print('mutantions bad for efnb2 binding, good for efnb3 binding:\n')
    display(efnb2_bad_efnb3_good)


find_biggest_differences(df_binding_effect_merge)

mutantions good for efnb2 binding, bad for efnb3 binding:

mutantions bad for efnb2 binding, good for efnb3 binding:

def find_good_binding_for_both(df):
    e2_good = df.sort_values(by='binding_mean_E2',ascending=False)
    e3_good = df.sort_values(by='binding_mean_E3',ascending=False)
    e2_good = e2_good.head(50)
    e3_good = e3_good.head(50)
    combo = pd.merge(e2_good,e3_good,on=['site','wildtype','mutant'],how='inner')
    display(combo)

find_good_binding_for_both(df_binding_effect_merge)

# Compare E2 and E3 binders
def find_highest_lowest(df):
    df["binding_diff"] = (df["binding_mean_E2"] - df["binding_mean_E3"]).abs()
    print(
        "These are the mutants with the biggest difference between EFNB2 and EFNB3:\n"
    )
    display(df.sort_values(by="binding_diff", ascending=False).head(10))

    # calculate aggregate differences
    agg_df = (
        df.groupby("site")[["binding_mean_E2", "binding_mean_E3", "binding_diff"]]
        .mean()
        .reset_index()
    )
    print("These are the sites with the biggest difference between EFNB2 and EFNB3:\n")
    display(agg_df.sort_values(by="binding_diff", ascending=False).head(5))


find_highest_lowest(df_binding_effect_merge)

These are the mutants with the biggest difference between EFNB2 and EFNB3:

These are the sites with the biggest difference between EFNB2 and EFNB3:

def plot_corr_binding_entry_updated(df, flag):
    df = df.copy().round(2)
    # Define interactive selectors for variant selection.
    variant_selector = alt.selection_point(
        on="mouseover", nearest=True, empty=False, fields=["site", "mutant"], value=0
    )
    # 'variant_selector_agg' is for aggregated selection based on 'site' only.
    variant_selector_agg = alt.selection_point(
        on="mouseover", nearest=True, empty=False, fields=["site"], value=0
    )

    # Initialize an empty list to store charts for each unique selection in 'df'.
    empty_chart = []
    
    # Loop through each unique cell selection in the DataFrame.
    for cell in list(df["selection"].unique()):
        # Filter DataFrame for the current selection.
        tmp_df = df[df["selection"] == cell]
        
        # Check if aggregation flag is True to aggregate data.
        if flag == True:
            # Aggregate data by 'site', summing 'binding_median' and 'effect', then reset index.
            agg_df = (
                tmp_df.groupby("site")[["binding_mean", "effect"]].mean().reset_index().round(2)
            )
            # Create a chart using aggregated data with specified encodings.
            chart = (
                alt.Chart(agg_df)
                .mark_point(stroke="black", filled=True)  # Use filled points with black stroke.
                .encode(
                    x=alt.X(
                        "effect",
                        title=f"Mean entry in CHO-{cell}",
                        axis=alt.Axis(grid=True),
                    ),
                    y=alt.Y(
                        "binding_mean",
                        title=f"Mean {cell} binding",
                        axis=alt.Axis(grid=True),
                    ),
                    # Dynamic opacity, size, strokeWidth, and color based on 'variant_selector_agg'.
                    opacity=alt.condition(
                        variant_selector_agg, alt.value(1), alt.value(0.2)
                    ),
                    size=alt.condition(
                        variant_selector_agg, alt.value(100), alt.value(50)
                    ),
                    strokeWidth=alt.condition(
                        variant_selector_agg, alt.value(1), alt.value(0)
                    ),
                    color=alt.condition(
                        variant_selector_agg, alt.value("orange"), alt.value("black")
                    ),
                    tooltip=["site", "binding_mean", "effect"],
                )
                .add_params(variant_selector_agg)
            )
            # Add the chart to the list.
            empty_chart.append(chart)

        else:
            # Create a chart for individual data points with specified encodings.
            chart = (
                alt.Chart(tmp_df)
                .mark_point(stroke="black", filled=True)
                .encode(
                    x=alt.X(
                        "effect", title=f"Entry in CHO-{cell}", axis=alt.Axis(grid=True)
                    ),
                    y=alt.Y(
                        "binding_mean",
                        title=f"{cell} binding",
                        axis=alt.Axis(grid=True),
                    ),
                    # Dynamic opacity, size, strokeWidth, and color based on 'variant_selector'.
                    opacity=alt.condition(
                        variant_selector, alt.value(1), alt.value(0.1)
                    ),
                    size=alt.condition(variant_selector, alt.value(50), alt.value(20)),
                    strokeWidth=alt.condition(
                        variant_selector, alt.value(1), alt.value(0)
                    ),
                    color=alt.condition(
                        variant_selector, alt.value("orange"), alt.value("black")
                    ),
                    tooltip=[
                        "site",
                        "wildtype",
                        "mutant",
                        "binding_mean",
                        "times_seen_binding",
                        "effect",
                    ],
                )
                .add_params(variant_selector)
            )
            # Add the chart to the list.
            empty_chart.append(chart)

    # Combine all charts horizontally with a title.
    combined_chart = alt.hconcat(
        *empty_chart, title=alt.Title("Correlation between binding and entry")
    )
    
    # Return the combined chart for display or further use.
    return combined_chart

# Generate and display plots for non-aggregated data.
entry_binding_corr_plot = plot_corr_binding_entry_updated(df_binding_effect_concat, False)
entry_binding_corr_plot.display()

# Save the plot 
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_plot.save(entry_binding_combined_corr_plot)

# Repeat for aggregated data.
entry_binding_corr_plot_agg = plot_corr_binding_entry_updated(df_binding_effect_concat, True)
entry_binding_corr_plot_agg.display()

def plot_entry_binding_corr_heatmap(df):
    empty_chart = []
    for cell in list(df["selection"].unique()):
        tmp_df = df[df["selection"] == cell]
        chart = (
            alt.Chart(tmp_df, title=f"{cell}")
            .mark_rect()
            .encode(
                x=alt.X(
                    "effect", title="Cell entry", axis=alt.Axis(values=[-2, -1, 0, 1])
                ).bin(maxbins=50),
                y=alt.Y(
                    "binding_mean",
                    title="Receptor binding",
                    axis=alt.Axis(values=[-4, -2, 0, 2]),
                ).bin(maxbins=50),
                color=alt.Color("count()", title="Count").scale(type='log'),
            )
        ).properties(width=200,height=200)
        empty_chart.append(chart)

    combined_chart = alt.hconcat(
        *empty_chart, 
    ).resolve_scale(y="shared", x="shared", color="shared")
    return combined_chart


entry_binding_corr_heat = plot_entry_binding_corr_heatmap(df_binding_effect_concat)
entry_binding_corr_heat.display()
if entry_binding_combined_corr_plot is not None:
    entry_binding_corr_heat.save(entry_binding_corr_heatmap)

def plot_entry_binding_corr(df):
    chart = (
        alt.Chart(df, title="Correlation Between Mutant Binding Scores")
        .mark_rect()
        .encode(
            x=alt.X(
                "binding_mean_E2",
                title="bEFNB2 binding",
                axis=alt.Axis(values=[-4,-2, 0, 2]),
            ).bin(maxbins=50),
            y=alt.Y(
                "binding_mean_E3",
                title="bEFNB3 binding",
                axis=alt.Axis(values=[-2, 0, 2]),
            ).bin(maxbins=50),
            color=alt.Color("count()", title="Count").scale(type='log'),
        )
    ).properties(width=200,height=200)
    return chart


entry_binding_corr_heatmap_1 = plot_entry_binding_corr(df_binding_effect_merge)
entry_binding_corr_heatmap_1.display()

def find_outliers(df):
    df = df.dropna().copy()
    #Calculate the best fit line
    slope, intercept = np.polyfit(df['binding_mean_E2'], df['binding_mean_E3'], 1)
    
    #compute residuals
    df['predicted_y'] = slope * df['binding_mean_E2'] + intercept
    df['residuals'] = df['binding_mean_E3'] - df['predicted_y']
    
    #identify outliers
    # calculate the mean and standard deviation of the residuals
    mean_residual = np.mean(df['residuals'])
    std_residual = np.std(df['residuals'])

    outlier_threshold = 4.5 #4.5 std deviations
    df['is_outlier'] = abs(df['residuals']) > (std_residual * outlier_threshold)
    
    # Filter the DataFrame to only include outliers
    outliers = df[df['is_outlier']]
    print(f'Here are the outlier mutations outside of a {outlier_threshold} standard deviation:\n')
    display(outliers)
    outliers_list = list(outliers['site'].unique())
    print(f' Here are the sites: \n {outliers_list}')
    return df,outliers_list

residuals_df,outliers_list = find_outliers(df_binding_effect_merge)

Here are the outlier mutations outside of a 4.5 standard deviation:

 Here are the sites: 
 [79, 137, 143, 161, 492, 530, 555, 580, 588]

def find_top_for_both(df):
    quantile_threshold = 0.99
    # Calculate the quantiles for both variables
    x_quantile = df['binding_mean_E2'].quantile(quantile_threshold)
    y_quantile = df['binding_mean_E3'].quantile(quantile_threshold)
    
    # Filter points that are above the quantile threshold
    df['meets_threshold'] = (df['binding_mean_E2'] >= x_quantile) & (df['binding_mean_E3'] >= y_quantile)
    subset = df.query('meets_threshold == True')
    top_mutants = list(subset['site'].unique())
    return df, top_mutants

top_residuals_df,top_mutants_list = find_top_for_both(residuals_df)
print(f' The sites with the top binding mutations are : \n {top_mutants_list}')
cleaned_df = top_residuals_df.query('meets_threshold == True')[['wildtype','site','mutant','binding_mean_E2','binding_mean_E3']].round(2)
print(f'Here are the specific mutations:\n')
display(cleaned_df)

 The sites with the top binding mutations are : 
 [104, 139, 211, 268, 306, 508, 553, 580, 589]
Here are the specific mutations:

def plot_affinity_BLI_mutants(df, highlight_conditions, color,subset=None):
    df = df.dropna().round(2).copy()
    df['site_mutant'] = df['site'].astype(str) + df['mutant'].astype(str)

    if subset is not None:
        df = df[df['site'].isin(subset)]

    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(
        df["binding_mean_E2"], df["binding_mean_E3"]
    )
    
    # make correlation chart
    chart = alt.Chart(df,title=alt.Title("Correlation Between Mutant Binding Scores",subtitle=f'r = {r_value:.2f}')
    ).mark_point(
        color="gray", 
        size=30, 
        opacity=0.4, 
        filled=True
    ).encode(
        x=alt.X("binding_mean_E2", title=("bEFNB2 binding"),axis=alt.Axis(tickCount=4)),
        y=alt.Y("binding_mean_E3", title=("bEFNB3 binding"),axis=alt.Axis(tickCount=4)),
        tooltip=[
            "site",
            "wildtype",
            "mutant",
            "binding_mean_E2",
            "binding_mean_E3",
            "effect_E2",
            "effect_E3",
            "binding_std_E2",
            "binding_std_E3",
            "times_seen_binding_E2",
            "times_seen_binding_E3"
        ],
    )

    #make colored circles for specific data
    highlight = chart.transform_filter(highlight_conditions).mark_point(
        color=color, size=60, opacity=1, filled=True, stroke='black', strokeWidth=1
    )
    #write text near the orange circles
    text_on_point = chart.transform_filter(highlight_conditions).mark_text(
            align='center',baseline='top',dy=-20,fontSize=16
    ).encode(text='site_mutant')
    
    min = int(df["binding_mean_E2"].min())
    max = int(df["binding_mean_E3"].max())
    text = (
        alt.Chart({"values": [{"x": min, "y": max, "text": f"r = {r_value:.2f}"}]})
        .mark_text(align="left", baseline="top", dx=-10, dy=-20)
        .encode(x=alt.X("x:Q"), y=alt.Y("y:Q"), text="text:N")
    )
    chart_and_text = chart + text_on_point + highlight #+ text
    return chart_and_text.properties(height=300,width=300)

# these are the data we want to show in orange circles
highlight_conditions = (
        (alt.datum.site == 244) & (alt.datum.mutant == 'W') |
        (alt.datum.site == 305) & (alt.datum.mutant == 'W') | 
        (alt.datum.site == 492) & (alt.datum.mutant == 'L') |
        (alt.datum.site == 507) & (alt.datum.mutant == 'I') |
        (alt.datum.site == 530) & (alt.datum.mutant == 'F') |
        (alt.datum.site == 553) & (alt.datum.mutant == 'W') |
        (alt.datum.site == 555) & (alt.datum.mutant == 'Y') |
        (alt.datum.site == 559) & (alt.datum.mutant == 'R') |
        (alt.datum.site == 588) & (alt.datum.mutant == 'V') 
)

E2_E3_corr_BLI_mutants = plot_affinity_BLI_mutants(df_binding_effect_merge,highlight_conditions,'#e49444')
E2_E3_corr_BLI_mutants.display()

highlight_conditions_neut = (
        (alt.datum.site == 333) & (alt.datum.mutant == 'Q') |
        (alt.datum.site == 492) & (alt.datum.mutant == 'R') | 
        (alt.datum.site == 507) & (alt.datum.mutant == 'I') |
        (alt.datum.site == 530) & (alt.datum.mutant == 'F') |
        (alt.datum.site == 553) & (alt.datum.mutant == 'W') |
        (alt.datum.site == 555) & (alt.datum.mutant == 'K') 
)
neut_muts = plot_affinity_BLI_mutants(df_binding_effect_merge,highlight_conditions_neut,'#5778a4')
neut_muts.display()

highlight_conditions = (
        (alt.datum.is_outlier == True)
)
E2_E3_corr = plot_affinity_BLI_mutants(residuals_df,highlight_conditions,'#d1615d')
E2_E3_corr.display()
if entry_binding_combined_corr_plot is not None:
    E2_E3_corr.save(E2_E3_correlation)

highlight_conditions = (
        (alt.datum.meets_threshold == True)
)
E2_E3_corr = plot_affinity_BLI_mutants(top_residuals_df.query('meets_threshold == True'),highlight_conditions,'#d1615d')
E2_E3_corr.display()

def plot_affinity_individual_mutants(df,mutant):
    df = df.dropna().round(2).copy()
    df = df[df['site'] == mutant]
    if df.empty:
        print('nothing here')
        pass
    else:
        wildtype_site = (df['wildtype'].astype(str) + df['site'].astype(str)).unique()[0]

        chart = alt.Chart(df,title=alt.Title(f"Site {wildtype_site}")
        ).mark_text(
            size=15
        ).encode(
            x=alt.X("binding_mean_E2", title=("bEFNB2 binding"),axis=alt.Axis(tickCount=3)),
            y=alt.Y("binding_mean_E3", title=("bEFNB3 binding"),axis=alt.Axis(tickCount=3)),
            text=alt.Text('mutant'),
            color=alt.Color('mutant_type_E2',title='Amino acid type'),
            tooltip=[
                "site",
                "wildtype",
                "mutant",
                "binding_mean_E2",
                "binding_mean_E3",
                "effect_E2",
                "effect_E3",
            ],
        )
    
        return chart.properties(height=200,width=200)

empty_charts = []
for site in top_mutants_list:
    indiv_graph = plot_affinity_individual_mutants(df_binding_effect_merge,site)
    empty_charts.append(indiv_graph)

top_sites = alt.vconcat(*empty_charts).resolve_scale(x='shared',y='shared',color='shared')
top_sites.display()

empty_charts = []
for site in outliers_list:
    if site < 180:
        pass
    else:
        indiv_graph = plot_affinity_individual_mutants(df_binding_effect_merge,site)
        empty_charts.append(indiv_graph)

outlier_sites = alt.vconcat(*empty_charts).resolve_scale(x='shared',y='shared',color='shared')
outlier_sites.display()

empty_charts = []
for site in config['contact_sites']:
    tmp_df = df_binding_effect_merge[df_binding_effect_merge['site'] == site]
    non_nan_in_effect_E2 = tmp_df['binding_mean_E2'].notnull().any()
    non_nan_in_effect_E3 = tmp_df['binding_mean_E3'].notnull().any()

    if non_nan_in_effect_E2 and non_nan_in_effect_E3:        
        contact_plots = plot_affinity_individual_mutants(tmp_df,site)
        empty_charts.append(contact_plots)
    else:
        pass

all_contact_plots = alt.vconcat(*empty_charts).resolve_scale(x='shared', y='shared')
all_contact_plots.display()

for site in list(range(580,590)):
    tmp_df = df_binding_effect_merge[df_binding_effect_merge['site'] == site]
    non_nan_in_effect_E2 = tmp_df['binding_mean_E2'].notnull().any()
    non_nan_in_effect_E3 = tmp_df['binding_mean_E3'].notnull().any()

    if non_nan_in_effect_E2 and non_nan_in_effect_E3:        
        test_plots = plot_affinity_individual_mutants(tmp_df,site)
        test_plots.display()
    else:
        pass

def plot_affinity_solid_mean(df):
    df = df.dropna()
    means = (
        df.groupby("site")
        .agg(
            {
                "effect_E2": "mean",
                "effect_E3": "mean",
                "binding_mean_E2": "mean",
                "binding_mean_E3": "mean",
                "wildtype": "first",
            }
        )
        .reset_index().round(2)
    )
    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(
        means["binding_mean_E2"], means["binding_mean_E3"]
    )
    r_value = float(r_value)
    chart = (
        alt.Chart(
            means,
            title=alt.Title(
                "Correlation between Aggregate Mutant Binding Scores",
                subtitle=f"r={r_value:.2f}",
            ),
        )
        .mark_point(size=50, opacity=1,filled=True,color='black')
        .encode(
            x=alt.X(
                "binding_mean_E2",
                title=("Mean bEFNB2 binding"),
                axis=alt.Axis(tickCount=3),
            ),
            y=alt.Y(
                "binding_mean_E3",
                title=("Mean bEFNB3 binding"),
                axis=alt.Axis(tickCount=3),
            ),
            tooltip=[
                "site",
                "wildtype",
                "binding_mean_E2",
                "binding_mean_E3",
                "effect_E2",
                "effect_E3",
            ],
        )
    )
    text = (
        alt.Chart({"values": [{"x": -3.5, "y": 0.5, "text": f"r = {r_value:.2f}"}]})
        .mark_text(align="left", baseline="top", dx=0, dy=0)
        .encode(x=alt.X("x:Q"), y=alt.Y("y:Q"), text="text:N")
    )
    chart_and_text = chart.properties(width=200,height=200)
    return chart_and_text


E2_E3_site_corr = plot_affinity_solid_mean(df_binding_effect_merge)
E2_E3_site_corr.display()
if entry_binding_combined_corr_plot is not None:
    E2_E3_site_corr.save(E2_E3_correlation_site)

def plot_affinity_by_site(df):
     # define ranges of different RBP regions
    barrel_ranges = {
        "Stalk": list(range(70, 148)),
        "Neck": list(range(148, 166)),
        "Linker": list(range(166, 178)),
        "Head": list(range(178, 603)),
    }
    
    custom_order = ["Stalk", "Neck", "Linker", "Head"] #custom order for color legend
    
    agg_means = [] #store aggregation 
    
    # For each barrel, filter the dataframe to the sites belonging to that barrel and then store the means
    for barrel, sites in barrel_ranges.items():
        subset = df[df["site"].isin(sites)]
        for _, row in subset.iterrows():
            agg_means.append(
                {"region": barrel, "binding_mean": row["binding_mean"], "site": row["site"],"selection": row["selection"]}
            )
        agg_means_df = pd.DataFrame(agg_means).round(2)
    
    # Setup interactivity
    variant_selector = alt.selection_point(
        on="mouseover", nearest=True, empty=False, fields=["site"], value=0
    )
    #make chart
    chart = alt.Chart(agg_means_df).mark_bar(stroke='black',size=1.5,binSpacing=0,color='black').encode(
        alt.X("site")
            .title("Site")
            .axis(tickCount=5,labelAngle=-90,grid=True),
            #.scale(domain=[70, 602]),
        
        alt.Y('mean(binding_mean)')
            .axis(tickCount=3)
            .title('Mean binding'),
        alt.Row('selection',title=None),
        tooltip=['site'],
        strokeWidth=alt.condition(variant_selector, alt.value(1), alt.value(0)),
        color=alt.Color('region',sort=custom_order,title='Region',scale=alt.Scale(range=['#5778a4', '#e49444', '#d1615d', '#85b6b2'])),
        #strokeColor=alt.condition(variant_selector, alt.value('orange'), alt.value('black')),


    ).properties(height=150, width=800).add_params(variant_selector).resolve_scale(y='independent')
    

    return chart


binding_by_site = plot_affinity_by_site(df_binding_effect_concat)
binding_by_site.display()
if entry_binding_combined_corr_plot is not None:
    binding_by_site.save(binding_by_site_plot)

def plot_affinity_by_contact_site(df, sites_to_show):
    # Filter the DataFrame based on the sites to show
    contact_df = df[df["site"].isin(sites_to_show)]
    
    # Define a selection for highlighting bars on hover
    variant_selector = alt.selection_point(on="mouseover", nearest=True, empty=False, fields=["site"])
    
    # Create the chart
    chart = alt.Chart(contact_df).mark_bar(stroke='black').encode(
        y=alt.Y("site:N", title="Site",sort='-x'),
        x=alt.X("max(binding_mean):Q", title="Max binding mutant at site"),
        color=alt.condition(variant_selector, alt.value("orange"), alt.value("black")),
        strokeWidth=alt.condition(variant_selector, alt.value(1), alt.value(0)),
        column=alt.Column('selection',title=None)
    ).add_params(variant_selector).properties(width=200, height=alt.Step(12)).resolve_scale(x='independent',y='shared').configure_header(
        labelFontSize=20,  
        labelAngle=0,
        labelAlign='center',
        labelAnchor='middle',
        labelFont='Helvetica Light',
        labelFontStyle='bold',
        labelPadding=3
    )
    
    return chart

contact_binding_by_site = plot_affinity_by_contact_site(df_binding_effect_concat, config["contact_sites"])
contact_binding_by_site.display()
if entry_binding_combined_corr_plot is not None:
    contact_binding_by_site.save(combined_contact_ranked_bar_output)

def make_bubble_binding_region(df):
    barrel_ranges = {
        "Stalk": list(range(70, 148)),
        "Neck": list(range(148, 166)),
        "Linker": list(range(166, 178)),
        "Head": list(range(178, 603)),
        "Receptor Contact": config["contact_sites"],
    }
    custom_order = ["Stalk", "Neck", "Linker", "Head", "Receptor Contact"]
    agg_means = []

    # For each barrel, filter the site_means dataframe to the sites belonging to that barrel and then store the means
    for barrel, sites in barrel_ranges.items():
        subset = df[df["site"].isin(sites)]
        for _, row in subset.iterrows():
            agg_means.append(
                {
                    "region": barrel,
                    "binding_mean": row["binding_mean"],
                    "site": row["site"],
                    "mutant": row["mutant"],
                    "selection": row["selection"],
                }
            )
        agg_means_df = pd.DataFrame(agg_means)

    variant_selector = alt.selection_point(
        on="mouseover", empty=False, nearest=True, fields=["site", "mutant"], value=1
    )

    chart = alt.Chart(agg_means_df).mark_point(stroke="black",filled=True).encode(
        x=alt.X(
            "region:O",
            sort=custom_order,
            title="RBP Region",
            axis=alt.Axis(labelAngle=-90),
        ),
        y=alt.Y(
            "binding_mean",
            title="Binding",
            axis=alt.Axis(tickCount=4),
        ),
        xOffset="random:Q",
        tooltip=["region", "binding_mean", "site", "mutant"],
        color=alt.condition(
            variant_selector, alt.value("orange"), alt.value("black")
        ),
        opacity=alt.condition(variant_selector, alt.value(1), alt.value(0.1)),
        strokeWidth=alt.condition(variant_selector, alt.value(2), alt.value(0)),
        size=alt.condition(variant_selector, alt.value(70), alt.value(20)),
        column=alt.Column('selection',title=None)
    ).transform_calculate(random="sqrt(-1*log(random()))*cos(2*PI*random())"
    ).properties(
        width=300, 
        height=300
    ).add_params(variant_selector
    ).resolve_scale(y='independent'
    ).configure_header(
        labelFontSize=20,  
        labelAngle=0,
        labelAlign='center',
        labelAnchor='middle',
        labelFont='Helvetica Light',
        labelFontStyle='bold',
        labelPadding=1
    )
    
    return chart


entry_region_bubble = make_bubble_binding_region(df_binding_effect_concat)
entry_region_bubble.display()
if entry_binding_combined_corr_plot is not None:
    entry_region_bubble.save(binding_region_bubble_plot)

def plot_effects_of_mutants(df):
    tmp_df = df.copy()
    tmp_df = tmp_df[
        #((tmp_df['site'] == 580) & (tmp_df['mutant'] == 'S')) |
        ((tmp_df['site'] == 598) & (tmp_df['mutant'] == 'G')) |
        #((tmp_df['site'] == 492) & (tmp_df['mutant'] == 'L')) |
        ((tmp_df['site'] == 553) & (tmp_df['mutant'] == 'W')) |
        #((tmp_df['site'] == 588) & (tmp_df['mutant'] == 'P')) |
        #((tmp_df['site'] == 492) & (tmp_df['mutant'] == 'R')) |
        #((tmp_df['site'] == 530) & (tmp_df['mutant'] == 'F')) |
        #((tmp_df['site'] == 492) & (tmp_df['mutant'] == 'K')) |
        #((tmp_df['site'] == 239) & (tmp_df['mutant'] == 'H')) |
        ((tmp_df['site'] == 211) & (tmp_df['mutant'] == 'F')) |
        ((tmp_df['site'] == 546) & (tmp_df['mutant'] == 'H')) |
        ((tmp_df['site'] == 143) & (tmp_df['mutant'] == 'Q')) |
        ((tmp_df['site'] == 331) & (tmp_df['mutant'] == 'E')) |
        ((tmp_df['site'] == 566) & (tmp_df['mutant'] == 'C')) 

        #((tmp_df['site'] == 589) & (tmp_df['mutant'] == 'G')) 
    ]
    tmp_df['label'] = (tmp_df['wildtype'].astype(str) + tmp_df['site'].astype(str) + tmp_df['mutant'].astype(str))

    tmp_df = tmp_df.sort_values(by='site')
    binding_chart = alt.Chart(tmp_df).mark_bar().encode(
        alt.X("label:N",title='Mutant',sort=None),
        alt.Y("binding_mean:Q",title='Binding score',axis=alt.Axis(tickCount=4)),
        xOffset="selection:N",
        color=alt.Color("selection:N",title='Receptor selection'),
        #row='selection'
    )
    binding_chart.resolve_scale(y='independent',x='shared').display()
plot_effects_of_mutants(df_binding_effect_concat)

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type
0	71	Q	E	0.1659	0.4309	3.5	-1.199	0.3996	5.250	1.0	negative
1	71	Q	F	-0.3429	0.5299	3.0	-0.947	0.6969	4.625	1.0	aromatic
2	71	Q	G	0.4657	0.7042	4.0	-1.374	0.5796	7.750	1.0	special

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type
0	71	Q	C	-0.1332	0.1525	3.0	-0.7227	0.7828	3.000	1.0	special
1	71	Q	D	-0.2937	0.3305	4.0	-0.3884	0.6369	3.429	1.0	negative
2	71	Q	E	-0.3527	0.1762	4.0	-0.2482	0.9791	4.571	1.0	negative

	site	wildtype	mutant	binding_mean_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	mutant_type_E2	binding_mean_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3	mutant_type_E3
0	71	Q	E	0.1659	0.4309	3.5	-1.199	0.3996	5.250	1.0	negative	-0.35270	0.17620	4.0	-0.2482	0.9791	4.571	1.0	negative
1	71	Q	F	-0.3429	0.5299	3.0	-0.947	0.6969	4.625	1.0	aromatic	-0.03982	0.12830	3.0	-0.4973	0.3080	3.286	1.0	aromatic
2	71	Q	G	0.4657	0.7042	4.0	-1.374	0.5796	7.750	1.0	special	-0.04107	0.05842	4.0	-1.3310	0.8316	4.714	1.0	special

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
0	71	Q	E	0.1659	0.4309	3.5	-1.199	0.3996	5.250	1.0	negative	bEFNB2
1	71	Q	F	-0.3429	0.5299	3.0	-0.947	0.6969	4.625	1.0	aromatic	bEFNB2
2	71	Q	G	0.4657	0.7042	4.0	-1.374	0.5796	7.750	1.0	special	bEFNB2

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
5330	501	E	K	-5.280	0.9284	24.50	-0.4975	0.7581	31.62	1.0	positive	bEFNB2
5709	532	A	V	-5.259	1.1400	16.25	-0.2039	0.4129	17.38	1.0	hydrophobic	bEFNB2
1744	236	R	H	-5.054	0.8828	22.00	-1.0930	0.6155	28.62	1.0	positive	bEFNB2
5200	490	Q	K	-4.966	1.0810	8.50	-0.5442	0.7343	10.62	1.0	positive	bEFNB2
5198	490	Q	H	-4.752	0.8619	8.25	-0.3961	0.7356	10.50	1.0	positive	bEFNB2

ephrin_binding.ipynb¶

Import libraries¶

Set working directory¶

Set hard paths for running in interactive mode¶

Run config files to setup altair theme and config variables¶

Import and merge data¶

Import the filtered binding and entry data for bEFNB2 and bEFNB3¶

Merge data¶

Calculate stats¶

Find sites with opposite effects on binding¶

Find the top overlapping binders for both bEFNB2 and bEFNB3¶

Find sites with the largest absolute difference in binding¶

Make plots¶

Make plots showing correlation between binding and entry for EFNB2 and EFNB3¶

Same plot as above, but slightly different format¶

Find correlations between bEFNB2 and bEFNB3 binding¶

Plot correlations of binding mutants in scatterplots, and color different subsets¶

First, find mutations that are outliers in the correlation between bEFNB2 and bEFNB3 binding¶

Find mutations in the top quantile of binding for bEFNB2 and bEFNB3 both¶

Plot correlations between individual mutational effects on binding, and color different subsets of sites¶

Sites selected for BLI validation¶

Sites selected for neutralization validations¶

Find outliers¶

Find top mutations for both bEFNB2 and bEFNB3¶

Plot correlations of mutants for individual sites of interest with letters of each mutant plotted¶

Plot amino acid letter correlation plots for the top sites¶

Plot amino acid letter correlation plots for the outlier sites¶

Now make for all contact sites¶

Make correlation amino acid letter plots for 580-590 loop¶

Plot correlations between each site for mean value¶

Make plot showing binding by site¶

Make bar chart showing max binding score for each contact residue¶

Make bubble plots for binding in different areas of receptor pocket¶

Plot effects of selected mutants on cell entry¶

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
6331	580	I	S	2.152	1.7680	4.75	-1.2740	0.7972	6.375	1.0	hydrophilic	bEFNB2
2934	331	N	E	2.144	1.2330	3.00	-1.0940	0.9094	3.500	1.0	negative	bEFNB2
6434	586	N	T	1.962	0.5779	5.25	-1.0610	0.9166	7.125	1.0	hydrophilic	bEFNB2
1433	211	G	F	1.957	0.5488	4.75	-0.9581	0.6185	5.125	1.0	aromatic	bEFNB2
6007	553	S	W	1.945	0.2867	10.00	-0.2906	0.4253	13.250	1.0	aromatic	bEFNB2

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
6068	558	A	V	-4.647	0.7295	17.25	0.21780	0.4495	17.620	1.0	hydrophobic	bEFNB2
6056	557	N	D	-4.627	0.3989	9.50	0.41380	0.5006	10.750	1.0	negative	bEFNB2
5704	532	A	L	-4.577	1.3510	6.00	0.07806	0.4481	7.625	1.0	hydrophobic	bEFNB2
5617	526	L	H	-4.546	0.7822	5.25	0.21200	0.4088	6.625	1.0	positive	bEFNB2
6349	581	Y	W	-4.519	1.0130	10.75	0.14230	0.4792	11.380	1.0	aromatic	bEFNB2

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
2234	282	C	S	1.609	1.0560	4.50	0.38130	0.3526	3.571	0.5	hydrophilic	bEFNB2
4683	450	Q	I	1.457	1.1940	5.00	0.12350	0.3928	6.000	1.0	hydrophobic	bEFNB2
777	164	N	S	1.297	1.1360	3.75	0.24060	0.2909	6.000	1.0	hydrophilic	bEFNB2
6140	564	N	K	1.259	0.4885	7.25	0.07777	0.4298	8.000	1.0	positive	bEFNB2
3591	376	K	F	1.208	1.7770	3.25	0.13220	0.2972	2.500	1.0	aromatic	bEFNB2

	site	binding_mean
470	564	15.086900
94	172	13.883700
490	586	13.369440
222	306	12.357770
247	331	11.730800
87	165	8.624400
236	320	8.574200
198	282	8.447490
206	290	8.318651
331	419	8.301810

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
6013	555	D	K	-2.147	1.2250	6.0	0.08504	0.4221	6.714	1.0	positive	bEFNB3
6016	555	D	R	-1.510	0.4205	4.0	0.07591	0.5260	4.143	1.0	positive	bEFNB3
5699	530	Q	L	-1.447	0.4772	3.5	-1.01900	0.8623	5.857	1.0	hydrophobic	bEFNB3
6301	583	T	R	-1.410	0.4465	5.5	-1.00500	0.4854	6.286	1.0	positive	bEFNB3
6298	583	T	K	-1.247	0.1132	5.0	-0.72780	0.6441	7.000	1.0	positive	bEFNB3

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
6347	589	R	G	2.006	0.65910	7.0	-0.9655	0.5763	8.286	1.0	special	bEFNB3
6267	580	I	M	1.337	0.53380	5.5	-0.8155	0.5307	6.714	1.0	hydrophobic	bEFNB3
2298	270	V	Q	1.329	1.02800	5.0	-0.8846	0.6509	5.429	1.0	hydrophilic	bEFNB3
2251	267	M	Q	1.291	1.37100	4.5	-0.7981	0.8175	5.667	1.0	hydrophilic	bEFNB3
5352	492	Q	L	1.261	0.06666	5.5	0.4833	0.1827	5.143	1.0	hydrophobic	bEFNB3

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
6013	555	D	K	-2.1470	1.2250	6.0	0.08504	0.42210	6.714	1.0	positive	bEFNB3
6016	555	D	R	-1.5100	0.4205	4.0	0.07591	0.52600	4.143	1.0	positive	bEFNB3
5702	530	Q	W	-0.9974	0.5473	3.0	0.01818	0.66470	3.286	1.0	aromatic	bEFNB3
6007	555	D	A	-0.8295	0.1999	3.5	0.37550	0.23450	3.714	1.0	hydrophobic	bEFNB3
4751	441	P	V	-0.7461	0.1815	5.5	0.37080	0.08612	5.286	1.0	hydrophobic	bEFNB3

	site	wildtype	mutant	binding_mean	binding_std	times_seen_binding	effect	effect_std	times_seen_cell_entry	frac_models	mutant_type	selection
5352	492	Q	L	1.2610	0.06666	5.5	0.48330	0.1827	5.143	1.0	hydrophobic	bEFNB3
1665	211	G	F	1.1690	0.28900	5.0	0.16370	0.5716	5.714	1.0	aromatic	bEFNB3
6429	598	P	G	1.1540	1.55900	4.5	0.23870	0.5146	5.429	1.0	special	bEFNB3
5987	553	S	W	0.9943	0.43520	9.5	0.16690	0.4060	9.857	1.0	aromatic	bEFNB3
6290	582	D	W	0.9761	0.33840	6.5	0.06342	0.3096	7.143	1.0	aromatic	bEFNB3

	site	binding_mean
489	589	11.707770
86	161	7.332800
468	564	6.721080
224	306	6.492740
59	132	6.168804
241	323	6.101950
469	566	5.968996
260	342	5.728920
128	204	5.653570
174	254	5.152500

	site	wildtype	mutant	binding_mean_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	mutant_type_E2	binding_mean_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3	mutant_type_E3
5890	546	L	H	1.5230	1.1450	5.25	-1.3690	0.4832	6.500	1.0	positive	-0.5431	0.6654	3.5	-0.85080	0.5440	5.143	1.0	positive
2512	303	P	C	1.3450	1.5770	5.00	-0.7173	0.5901	4.875	1.0	special	-0.5659	0.0975	4.0	-0.69680	0.5942	4.857	1.0	special
109	79	N	S	1.2920	1.6520	3.00	-0.6820	0.5704	2.375	1.0	hydrophilic	-0.6464	0.2595	3.0	-0.12940	0.5688	3.429	1.0	hydrophilic
89	78	D	I	0.6586	0.6340	5.25	-0.9271	0.4006	6.500	1.0	hydrophobic	-0.5665	0.7552	5.0	-0.51670	0.8447	5.143	1.0	hydrophobic
1612	224	M	Q	0.6473	0.8796	3.50	0.1696	0.3110	2.625	1.0	hydrophilic	-0.5806	0.3813	3.5	0.20550	0.2920	4.286	1.0	hydrophilic
5552	520	I	G	0.5914	1.0130	5.00	-1.3920	0.3251	7.875	1.0	special	-0.5305	0.1476	5.5	-1.30900	0.3187	7.000	1.0	special
980	178	V	T	0.5288	0.7257	3.75	-0.4327	0.8737	4.625	1.0	hydrophilic	-0.5130	0.5995	3.0	-0.01921	0.4254	3.571	1.0	hydrophilic

	site	wildtype	mutant	binding_mean_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	mutant_type_E2	binding_mean_E3	binding_std_E3	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3	mutant_type_E3	binding_diff
5682	530	Q	F	-4.015	0.46650	6.25	0.49310	0.2289	5.250	1.0	aromatic	0.084370	0.06198	6.0	0.3592	0.2738	6.286	1.0	aromatic	4.099370
5207	490	Q	Y	-4.290	0.90020	5.50	-0.87840	0.7489	6.375	1.0	aromatic	-0.461000	0.57400	5.5	-0.3137	0.7967	5.714	1.0	aromatic	3.829000
5212	491	S	H	-4.505	0.93500	8.50	0.17270	0.3082	9.750	1.0	positive	-0.871800	0.78980	7.0	-1.3810	0.4865	8.286	1.0	positive	3.633200
5211	491	S	G	-4.181	0.59490	6.50	-0.05616	0.6193	8.375	1.0	special	-0.562100	0.04726	5.5	-0.2358	0.6084	6.286	1.0	special	3.618900
5206	490	Q	W	-4.205	1.00500	3.75	0.25840	0.3965	4.625	1.0	aromatic	-0.586500	0.32260	3.0	-0.1506	0.4575	3.286	1.0	aromatic	3.618500
6465	588	I	P	-2.349	0.84050	5.25	-0.30780	0.2743	4.875	1.0	special	1.183000	0.25530	5.5	-0.6131	0.3819	5.429	1.0	special	3.532000
5231	492	Q	K	-3.523	0.54860	11.00	0.39690	0.2904	11.620	1.0	positive	0.006126	0.07740	10.0	0.3291	0.2404	11.570	1.0	positive	3.529126
5235	492	Q	R	-2.823	0.61110	19.25	-0.10820	0.2778	23.000	1.0	positive	0.643700	0.05388	20.0	0.5095	0.1192	20.570	1.0	positive	3.466700
1786	239	S	H	-3.440	0.08816	5.25	0.36800	0.3143	4.500	1.0	positive	-0.041160	0.83220	4.0	-1.2190	0.6930	5.000	1.0	positive	3.398840
5691	530	Q	V	-4.327	0.83770	6.00	0.16180	0.3502	8.500	1.0	hydrophobic	-0.987000	0.89600	5.5	-1.4430	0.7389	7.000	1.0	hydrophobic	3.340000

	site	binding_mean_E2	binding_mean_E3	binding_diff
415	505	-3.928250	-0.770900	2.897100
439	530	-3.663994	-0.559266	2.797646
405	491	-3.603113	-0.364667	2.785117
495	588	-3.567800	0.037600	2.686600
404	490	-3.812286	-0.370980	2.497620

	site	wildtype	mutant	binding_mean_E2	binding_std_E2	times_seen_binding_E2	effect_E2	effect_std_E2	times_seen_cell_entry_E2	frac_models_E2	...	times_seen_binding_E3	effect_E3	effect_std_E3	times_seen_cell_entry_E3	frac_models_E3	mutant_type_E3	binding_diff	predicted_y	residuals	is_outlier
109	79	N	S	1.2920	1.6520	3.00	-0.6820	0.5704	2.375	1.0	...	3.0	-0.12940	0.5688	3.429	1.0	hydrophilic	1.93840	0.271046	-0.917446	True
555	137	S	D	-0.4362	0.8381	4.00	-1.2230	0.4498	7.000	1.0	...	4.0	-0.41160	0.7602	3.571	1.0	negative	1.23990	-0.118953	0.922653	True
609	143	N	Q	0.4306	0.6943	5.25	-0.1664	0.5468	8.250	1.0	...	4.5	-0.35540	0.7932	4.286	1.0	hydrophilic	1.43260	0.076656	-1.078656	True
752	161	S	E	0.5686	0.6514	2.50	-1.2950	0.6283	5.750	1.0	...	3.0	-0.14880	0.4404	3.143	1.0	negative	0.54040	0.107798	1.001202	True
5232	492	Q	L	-0.2126	0.2587	5.50	0.4493	0.3064	4.625	1.0	...	5.5	0.48330	0.1827	5.143	1.0	hydrophobic	1.47360	-0.068493	1.329493	True
5235	492	Q	R	-2.8230	0.6111	19.25	-0.1082	0.2778	23.000	1.0	...	20.0	0.50950	0.1192	20.570	1.0	positive	3.46670	-0.657576	1.301276	True
5682	530	Q	F	-4.0150	0.4665	6.25	0.4931	0.2289	5.250	1.0	...	6.0	0.35920	0.2738	6.286	1.0	aromatic	4.09937	-0.926572	1.010942	True
6032	555	D	K	-3.3210	0.6425	8.00	0.2361	0.3405	10.000	1.0	...	6.0	0.08504	0.4221	6.714	1.0	positive	1.17400	-0.769958	-1.377042	True
6035	555	D	R	-2.4650	1.6330	3.00	0.3892	0.1980	4.500	1.0	...	4.0	0.07591	0.5260	4.143	1.0	positive	0.95500	-0.576787	-0.933213	True
6321	580	I	C	0.5076	0.9803	3.25	-1.4830	0.4867	8.000	1.0	...	4.0	-0.31540	0.4011	4.857	1.0	special	0.73340	0.094033	1.146967	True
6465	588	I	P	-2.3490	0.8405	5.25	-0.3078	0.2743	4.875	1.0	...	5.5	-0.61310	0.3819	5.429	1.0	special	3.53200	-0.550609	1.733609	True

	wildtype	site	mutant	binding_mean_E2	binding_mean_E3
386	E	104	T	1.93	0.65
582	N	139	W	1.93	1.00
1433	G	211	F	1.96	1.17
2015	T	268	W	1.52	0.77
2561	N	306	M	1.49	0.66
2564	N	306	R	1.52	0.90
2566	N	306	T	1.39	0.72
5398	Y	508	Q	1.93	0.64
6007	S	553	W	1.94	0.99
6331	I	580	S	2.15	0.82
6480	R	589	V	1.67	0.73